Spatial Data Mining

Foreword

Spatial data sets are at the heart of a variety of scientific and
engineering domains, from computational fluid dynamics to distributed
sensor and actuator networks to structural bioinformatics. Rapid
advances in simulation and experimentation in these domains are
yielding an increasing reliance on efficient and effective spatial
data mining algorithms. These developments demand effective
cross-fertilization and consolidation of computational techniques from
fields such as data mining, qualitative spatial reasoning, scientific
computing, and statistical methodology, in the context of significant
applications. The SIAM-DM 2006 Workshop on Spatial Data Mining
provides a forum such an exchange.

In order to focus the discussion, we have formulated a set of
challenge problems in one significant application area: pandemic
detection and response. Pandemic influenza viruses have demonstrated
an ability to spread worldwide within months or even weeks.
Controlling the spread of a pandemic requires early detection via
appropriate surveillance, along with implementation of corresponding
control measures (e.g., isolation of cases, quarantine of contacts,
antiviral drug treatment and prophylaxis). Spatial data mining
challenges include developing a synthetic time-varying social network
capturing collocation and effective contact patterns, conducting
model-based data aggregation using the derived network in order to
identify the onset of disease and other qualitative indicators of
disease spread, and using the structure of the network to identify
critical individuals and locations for targeted detection and
vaccination.

The workshop includes an introduction to challenges in pandemic
preparedness by Madhav Marathe, an invited talk, presentations of four
papers addressing spatial data mining challenges in the pandemic
preparedness context and two papers addressing general issues in
spatial data mining, and a discussion period for comparing,
contrasting, identifying emergent themes, and so forth.

The program chairs are Chris Bailey-Kellogg and Naren Ramakrishnan,
and the program committee is as follows:

Chris Bailey-Kellogg, Dartmouth (co-chair)
Jochen Garcke, Australian National University

Jiawei Han, University of Illinois at Urbana-Champaign
George Karypis, University of Minnesota
Tao Li, Florida International University
Madhav Marathe, Virginia Bioinformatics Institute
Naren Ramakrishnan, Virginia Tech (co-chair)
Srinivasan Parthasarathy, The Ohio State University
Shashi Shekhar, University of Minnesota
Feng Zhao, Microsoft Research